YouTube Metadata Scraper

YouTube Metadata Scraper — Search, Comments and Transcripts

YouTube Metadata Scraper collects video metadata, channel context, top-level comments and caption transcripts from direct video links or from a keyword search of YouTube's own results — one JSON row per video, with comments and transcripts nested right inside it. Every response is structured JSON, ready to pass directly to an LLM, index into a vector store, or feed a monitoring pipeline. No YouTube account, cookie, or login is read anywhere in the input schema or used by the request layer. Point it at a list of URLs, a set of search phrases, or both in the same run, and get a dataset back that already links the video, its channel, its top comments and its captions together.

What is YouTube Metadata Scraper?

YouTube Metadata Scraper is an Apify Actor that resolves videos two ways in the same run — from a list of urls (any watch, Shorts, live, embed, or youtu.be link, or a bare 11-character video ID) and/or from keywords, which it expands by paging through YouTube's own search-results endpoint. For every resolved video it returns one dataset row combining video metadata, channel details, up to max_comments top-level comments and, when available, the caption transcript — as plain text or as timed segments. No YouTube account or login is required for any of it: the Actor reads only what a logged-out visitor sees. Key capabilities:

• Dual discovery — direct urls for known videos, keywords for open-ended search, deduplicated and processed together in one run.
• Exact engagement counters — viewCount and likeCount are read as exact integers, not YouTube's abbreviated on-page text.
• The real channel @handle — channelUsername is the actual handle scraped from the page, never guessed from the display name.
• Top-level comment threads — comment text, author, like count, publish time and reply count, up to the limit you set.
• Caption transcripts — plain-text or timestamped segments, with human-readable language names and duplicate tracks removed.
• Query controls — sort_order for keyword runs, max_results_per_keyword for how deep to page, and separate toggles for auto-generated and non-English captions.

What data can you get with YouTube Metadata Scraper?

Every run can return four data groups inside a single row per video.

Result Type	Extracted Fields	Primary Use Case
Video metadata	id, url, title, description, lengthInSeconds, uploadDatetime, publish_date_since, category, tags, thumbnail, thumbnails_urls, viewCount, likeCount, commentCount, commentCountIsApprox, is_live_now, description_links, is_comments_off, is_members_only, is_paid_content	Content research, SEO and performance analysis
Channel context	channelID, channelURL, channelUsername, channelDisplayName, channelSubscribers, channelSubscribersCount, author_avatar_url, is_verified	Creator identification, influencer vetting, outreach
Comments (comments array)	id, text, author, authorChannelId, likeCount, publishedTime, replyCount	Audience sentiment and engagement research
Transcripts (transcripts array)	language, languageCode, languageName, isAutoGenerated, characterCount, content	Content repurposing, subtitle generation, RAG ingestion, keyword search

Comments and transcripts alongside search-driven discovery

The reason this Actor covers keyword discovery, comments and transcripts in one run is that none of it needs a second tool or a second cost: pass search phrases in keywords, and every video that surfaces already carries its own comments and transcripts arrays in the same row, controlled by max_comments, include_transcripts and the caption-language toggles. A comment row inside comments carries the commenter's display name, channel ID, like count and reply count; a transcript row inside transcripts carries the caption language, whether it was auto-generated, its character count, and the text itself — as one joined string when transcript_format is text, or as a list of {start, dur, text} segments when it is timestamps.

{
  "id": "M7lc1UVf-VE",
  "title": "Google I/O Keynote Highlights",
  "viewCount": 812340,
  "comments": [
    { "id": "UgzAbc123", "text": "Great overview, thanks!", "author": "Dana Park", "authorChannelId": "UCabc123", "likeCount": 42, "publishedTime": "3 months ago", "replyCount": 2 }
  ],
  "transcripts": [
    { "language": "EN", "languageCode": "en", "languageName": "English", "isAutoGenerated": false, "characterCount": 8120, "content": "Welcome everyone to this year's keynote..." }
  ]
}

The real @handle in channelUsername

Every channel is resolved to its actual @handle, read from the page's own canonical channel link rather than guessed from the display name — a display name like "Bro Code" does not reliably map to its real handle (@BroCodez, in that example), and a synthesized guess produces a channel URL that doesn't exist. channelUsername is empty only when YouTube itself exposes no handle for that channel, such as some auto-generated artist channels — it is never invented. channelDisplayName still carries the human-readable name shown under the video for anything that needs it.

How does YouTube Metadata Scraper differ from the official YouTube Data API?

YouTube Data API v3 is Google's own public API for video and channel metadata, and it returns real, accurate statistics for videos and channels once you have a project and a key. Where it falls short of this Actor is search volume, keyword-to-transcript coverage, and caption access for videos you don't own.

Feature	YouTube Data API v3	YouTube Metadata Scraper
Setup	Google Cloud project, API key or OAuth client	Apify account only — no Google credentials
Caption/transcript text for a video you don't own	captions.download "requires the user to have permission to edit the video" (Google Developers docs) — third-party public videos are effectively off-limits	Reads caption text directly from the public watch page for any video, regardless of ownership
Combined coverage per lookup	Video stats, channel data, comment threads and (owned) captions each live behind separate resources (videos, channels, commentThreads, captions), metered from the same shared project quota	Video metadata, channel context, comments and transcript all come back in one row from one Actor run
Keyword-driven discovery	search.list returns matching videos, but every call draws from the same per-project daily quota that all your other API calls also consume	keywords pages through YouTube's own search results with no per-call quota to manage
Result delivery	Raw JSON responses only — you build your own storage and export layer	Results land in an Apify dataset, exportable as JSON, CSV, Excel, or XML, or read live via the Apify API

The official API is the right choice when you already manage a Google Cloud project, need captions for videos your own channel owns, or want a first-party SLA for your own product. YouTube Metadata Scraper is the better fit for ad hoc or bulk research across videos you don't own — keyword discovery, comments and caption text together, with no OAuth flow or quota dashboard to build.

What is the difference between a YouTube metadata scraper and a YouTube transcript scraper?

A YouTube transcript scraper answers one narrow question — what does this video's caption track say — and returns caption text with nothing else attached. A YouTube metadata scraper answers a broader one — what is this video, who made it, and how is it performing — but the two are often sold as separate tools, sometimes even by the same vendor, because caption extraction and metadata extraction historically ran through different code paths. That split matters once a workflow needs both: knowing a video is trending (viewCount, likeCount, commentCount) is what tells you it's worth reading the transcript for, and the transcript is what tells you why. YouTube Metadata Scraper returns both in the same JSON row — transcripts sits alongside viewCount, channelUsername and comments on the identical object — so there is no second tool, second run, or second cost to go from "this video is worth reading" to "here is what it says."

How to scrape YouTube with YouTube Metadata Scraper?

1. Open the Actor's page on the Apify Store and start a new run.
2. Enter one or more video links or IDs in urls, and/or one or more search phrases in keywords — at least one of the two is required.
3. Set sort_order and max_results_per_keyword to control how keyword results are ranked and how many videos are collected per phrase.
4. Set max_comments, include_transcripts, transcript_format, include_english_auto and include_non_english for how much comment and caption depth to collect per video.
5. Start the run, then download or stream results from the dataset as JSON, CSV, Excel, or XML.

Example run input using the Actor's real parameter names:

{
  "urls": ["https://www.youtube.com/watch?v=M7lc1UVf-VE"],
  "keywords": ["react tutorial for beginners"],
  "sort_order": "view_count",
  "max_results_per_keyword": 20,
  "max_comments": 10,
  "include_transcripts": true,
  "transcript_format": "text",
  "include_english_auto": true,
  "include_non_english": false
}

How to run multiple queries in one job

Both urls and keywords accept arrays, so a single run can process a batch of known videos and a batch of search phrases together — there is no need to launch a separate run per video or per keyword. Video IDs resolved from urls and video IDs discovered from every keyword in keywords are combined and deduplicated before processing starts, so a video that shows up both directly and inside a keyword's search results is only fetched, and only charged, once.

⬇️ Input

The Actor accepts direct video links or IDs, keyword search phrases, or both — plus controls for keyword ranking, comment depth, transcript coverage and proxy behavior. All ten fields below come directly from the input schema; none are required on their own, but at least one of urls or keywords must be supplied.

Parameter	Required	Type	Description	Example Value
urls	No	array	Video URLs or IDs, one entry per video. Accepts youtube.com/watch?v=…, youtu.be/…, youtube.com/shorts/…, youtube.com/live/…, youtube.com/embed/…, youtube.com/v/…, and the bare 11-character video ID. Leave empty for keyword-only discovery.	["https://www.youtube.com/watch?v=M7lc1UVf-VE"]
keywords	No	array	YouTube search phrases, one per line. sort_order and max_results_per_keyword control ranking and volume for these. Shorts appearing inside search results are skipped; a Shorts watch URL pasted into urls is scraped normally.	["react tutorial for beginners"]
sort_order	No	string (enum: relevance, upload_date, view_count, rating; default relevance)	Applies to keyword runs only. YouTube's live sort menu currently honours only relevance and view_count; upload_date and rating are still sent and accepted for input compatibility, but YouTube ignores them and the run logs a warning instead of pretending otherwise.	"view_count"
max_results_per_keyword	No	integer (min 1, max 500, default 10)	Videos taken per keyword, paging through YouTube's search continuations. YouTube stops serving new results well before any high number — the measured deep-page ceiling is roughly 133–484 videos per keyword and varies by phrase, so a value above that returns whatever YouTube still has.	20
max_comments	No	integer (min 0, max 5000, default 2)	Top-level comments to store per video, verified exact from 0 up to 300+. Set 0 to skip comments entirely. Reply bodies, comment sort order, and comment permalinks are not collected.	10
include_transcripts	No	boolean (default true)	Attach caption text for each video when YouTube exposes captions for that upload. Duplicate tracks carrying identical text are removed; the download is capped at 6 languages and 150 seconds per video.	true
transcript_format	No	string (enum: text, timestamps; default text)	text puts one paragraph string in content; timestamps puts a list of {start, dur, text} segments there instead.	"text"
include_english_auto	No	boolean (default false)	Also allow YouTube auto-captions in English alongside creator-uploaded captions — this is what lifts transcript coverage on videos with no manual captions.	true
include_non_english	No	boolean (default false)	Allow non-English manual or auto tracks. Popular videos expose dozens of auto-translated languages; the run keeps the first 6 distinct tracks and stops there.	false
proxyConfiguration	No	object (default: {"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountryCode": "US"})	Your proxy choice is honoured request by request. Residential + US is the default and is strongly recommended: on a datacenter exit, roughly 83% of watch pages come back as an HTTP 200 sign-in page with no title, and transcripts return nothing at all. If a page comes back walled, the run escalates to Residential/US on its own and logs it.	{ "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountryCode": "US" }

Example JSON input

{
  "urls": [
    "https://www.youtube.com/watch?v=M7lc1UVf-VE",
    "https://www.youtube.com/shorts/8hly31xKli0"
  ],
  "keywords": ["react tutorial for beginners", "docker compose explained"],
  "sort_order": "relevance",
  "max_results_per_keyword": 15,
  "max_comments": 5,
  "include_transcripts": true,
  "transcript_format": "timestamps",
  "include_english_auto": true,
  "include_non_english": false,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountryCode": "US"
  }
}

Common pitfall: max_comments controls how many items land in each row's comments array, but commentCount is a separate field — it is YouTube's own displayed comment total for the video, read straight off the page, and is not shaped by max_comments at all. Setting max_comments: 5 still reports the video's real full commentCount, alongside exactly 5 (or fewer, if the video has fewer) items in comments.

⬆️ Output

Results are typed, normalized JSON with a consistent field set across every row, exportable from the Apify platform in JSON, CSV, Excel, XML, or HTML, or read live through the Apify API. Comments and transcripts arrive nested inside their parent video row rather than as separate rows, so a single record carries everything collected for that video.

Every dataset row is billed as one row_result event — but only when the row is genuinely populated. A row is charged when it carries a title, a viewCount greater than 0, and either a channelID or a channelDisplayName. A video that fails that check — for example a page YouTube served back in a degraded or partially blocked state, with fields the Actor could not read — is still saved to the dataset for visibility, but is pushed without the row_result charge. Check the Actor's Pricing tab on the Apify Store for the current per-event rate.

Scraped results

[
  {
    "id": "M7lc1UVf-VE",
    "url": "https://www.youtube.com/watch?v=M7lc1UVf-VE",
    "title": "Google I/O Keynote Highlights",
    "description": "A look back at the announcements from this year's keynote.",
    "lengthInSeconds": 1523,
    "uploadDatetime": "2024-05-14T17:00:00-07:00",
    "publish_date_since": "1 year ago",
    "category": "Science & Technology",
    "tags": ["google", "keynote", "developers"],
    "thumbnail": "https://i.ytimg.com/vi/M7lc1UVf-VE/hqdefault.jpg",
    "thumbnails_urls": ["https://i.ytimg.com/vi/M7lc1UVf-VE/hqdefault.jpg"],
    "channelID": "UC_x5XG1OV2P6uZZ5FSM9Ttw",
    "channelURL": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
    "channelUsername": "GoogleDevelopers",
    "channelDisplayName": "Google for Developers",
    "channelSubscribers": "2.5M",
    "channelSubscribersCount": 2500000,
    "author_avatar_url": "https://yt3.ggpht.com/example-avatar",
    "is_verified": true,
    "viewCount": 812340,
    "likeCount": 15230,
    "commentCount": 940,
    "commentCountIsApprox": true,
    "is_live_now": false,
    "description_links": ["https://developers.google.com"],
    "is_comments_off": false,
    "is_members_only": false,
    "is_paid_content": false,
    "transcripts": [
      { "language": "EN", "languageCode": "en", "languageName": "English", "isAutoGenerated": false, "characterCount": 8120, "content": "Welcome everyone to this year's keynote..." }
    ],
    "comments": [
      { "id": "UgzAbc123", "text": "Great overview, thanks!", "author": "Dana Park", "authorChannelId": "UCabc123", "likeCount": 42, "publishedTime": "3 months ago", "replyCount": 2 }
    ]
  },
  {
    "id": "8hly31xKli0",
    "url": "https://www.youtube.com/watch?v=8hly31xKli0",
    "title": "60-Second Docker Compose Tip",
    "description": "A quick tip for structuring your compose file.",
    "lengthInSeconds": 58,
    "uploadDatetime": "2025-02-02T09:15:00-08:00",
    "publish_date_since": "5 months ago",
    "category": "Education",
    "tags": ["docker", "devops"],
    "thumbnail": "https://i.ytimg.com/vi/8hly31xKli0/hqdefault.jpg",
    "thumbnails_urls": ["https://i.ytimg.com/vi/8hly31xKli0/hqdefault.jpg"],
    "channelID": "UCexample2",
    "channelURL": "https://www.youtube.com/channel/UCexample2",
    "channelUsername": "DevOpsDaily",
    "channelDisplayName": "DevOps Daily",
    "channelSubscribers": "184K",
    "channelSubscribersCount": 184000,
    "author_avatar_url": "https://yt3.ggpht.com/example-avatar-2",
    "is_verified": false,
    "viewCount": 43210,
    "likeCount": 2110,
    "commentCount": 76,
    "commentCountIsApprox": true,
    "is_live_now": false,
    "description_links": [],
    "is_comments_off": false,
    "is_members_only": false,
    "is_paid_content": false,
    "transcripts": [],
    "comments": [
      { "id": "UgzDef456", "text": "Didn't know about this, thanks!", "author": "Marco Reyes", "authorChannelId": "UCdef456", "likeCount": 5, "publishedTime": "4 months ago", "replyCount": 0 }
    ]
  },
  {
    "id": "jNQXAC9IVRw",
    "url": "https://www.youtube.com/watch?v=jNQXAC9IVRw",
    "title": "Backyard Coffee Roasting, Explained",
    "description": "How I roast small batches at home.",
    "lengthInSeconds": 641,
    "uploadDatetime": "2023-11-09T12:30:00-08:00",
    "publish_date_since": "2 years ago",
    "category": "Howto & Style",
    "tags": ["coffee", "roasting"],
    "thumbnail": "https://i.ytimg.com/vi/jNQXAC9IVRw/hqdefault.jpg",
    "thumbnails_urls": ["https://i.ytimg.com/vi/jNQXAC9IVRw/hqdefault.jpg"],
    "channelID": "UCexample3",
    "channelURL": "https://www.youtube.com/channel/UCexample3",
    "channelUsername": "",
    "channelDisplayName": "Home Roast Journal",
    "channelSubscribers": "12.3K",
    "channelSubscribersCount": 12300,
    "author_avatar_url": "https://yt3.ggpht.com/example-avatar-3",
    "is_verified": false,
    "viewCount": 9840,
    "likeCount": 512,
    "commentCount": 61,
    "commentCountIsApprox": true,
    "is_live_now": false,
    "description_links": [],
    "is_comments_off": false,
    "is_members_only": false,
    "is_paid_content": false,
    "transcripts": [
      { "language": "EN-ORIG (auto-generated)", "languageCode": "en", "languageName": "English (auto-generated)", "isAutoGenerated": true, "characterCount": 4310, "content": "so today we're going to talk about roasting at home..." }
    ],
    "comments": []
  }
]

How can I use the data extracted with YouTube Metadata Scraper?

• Content strategists and SEO researchers: compare title, tags, description and viewCount across a keyword's top results to see which formats and topics are actually ranking.
• AI engineers and LLM developers: have an agent call the Actor with a topic in keywords, receive structured video, comment and transcript JSON back, and pass content from transcripts to a model as grounded context for summarization or Q&A.
• Market and competitor researchers: export comments for a channel's recent videos and run your own sentiment or keyword pass over text, without a separate comment scraper.
• Influencer and creator vetting teams: use channelUsername, channelSubscribersCount and is_verified to confirm a creator's real handle and scale before reaching out.

📈 How do you monitor video engagement over time?

Video engagement monitoring means running the same urls or keywords set on a recurring schedule and comparing each new run's rows against the previous run's for the same video. Because viewCount, likeCount, commentCount and channelSubscribersCount are all read fresh from that run's own page fetch, a video whose viewCount or commentCount climbs sharply between runs is a genuine growth signal, not a scraping artifact. The fields worth diffing are viewCount and likeCount on the video itself for overall traction, commentCount for discussion volume, and the comments array's newest entries (by publishedTime) for what people are saying right now.

A practical workflow: run the same keyword or URL set daily or weekly, key each run's rows by id plus the run date, then alert when a tracked video's viewCount or commentCount jumps sharply between runs, or when a new video appears in a monitored keyword's results for the first time. This Actor has no scheduler of its own — recurring runs are set up with Apify's Schedule feature on the Actor's page, which triggers a fresh run on the interval you configure and writes each run to its own dataset for comparison.

Integrate YouTube Metadata Scraper and automate your workflow

YouTube Metadata Scraper works with any language or tool that can send an HTTP request, and with the Apify API and client libraries for scripted access.

REST API with Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
    "urls": ["https://www.youtube.com/watch?v=M7lc1UVf-VE"],
    "keywords": ["react tutorial for beginners"],
    "max_results_per_keyword": 20,
    "max_comments": 10,
    "include_transcripts": True,
    "include_english_auto": True,
}

run = client.actor("youtube-metadata-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], item["viewCount"], len(item.get("comments") or []))

MCP for query-grounded AI agents

Any Apify Actor, including this one, is callable through Apify's hosted MCP server at mcp.apify.com, or locally via npx @apify/actors-mcp-server with an APIFY_TOKEN environment variable. An MCP-connected agent can call the Actor as a tool — passing a topic or a video list — receive structured metadata, comment and transcript JSON back, and generate a grounded answer about a video or a search topic without the agent developer writing any YouTube-specific scraping code. Compatible with Claude Desktop, Claude Code, Cursor, and other MCP clients.

Scheduled monitoring and delivery

The Actor itself has no push-webhook feature, but the Apify platform supports scheduled runs (via Schedules) and run-completion webhooks for any Actor, including this one — so a recurring keyword or channel check can be wired to notify Slack, trigger a Zapier/Make workflow, or write to a data warehouse whenever a scheduled run finishes.

Is it legal to scrape YouTube video metadata?

Scraping publicly visible web pages is generally lawful in the United States — courts have held that accessing data not gated behind a login is not a violation of the Computer Fraud and Abuse Act, most notably in hiQ Labs, Inc. v. LinkedIn Corp., 9th Circuit, 2019. YouTube Metadata Scraper reads only content YouTube serves to a logged-out visitor: video metadata, channel details, top-level comments and captions are all public-facing, and no login, cookie, or credential is used or accepted anywhere in its input. Video and channel metadata is business/product data governed by YouTube's own Terms of Service rather than personal-data law; comment author display names and channel IDs, however, are personal data in many jurisdictions, so GDPR, CCPA, or equivalent regimes govern how you store and process the comments array once collected. Scraping for AI training and scraping for operational monitoring carry different risk profiles. Consult your legal team for commercial use cases involving bulk data storage.

❓ Frequently asked questions

Can I supply both URLs and keywords in the same run?

Yes. urls and keywords are independent and can both be populated in one run — the Actor resolves video IDs from both sources, deduplicates them, and processes the combined list together in a single dataset.

What does sort_order actually change?

For keyword runs, it's meant to re-rank YouTube's search results — but only relevance and view_count currently do anything. upload_date and rating are still sent to YouTube exactly as documented, but YouTube's live sort menu no longer honours either one, so results come back unordered by those criteria; the run logs a warning rather than pretending they worked. Both values stay in the enum so a saved input using them doesn't break.

How does YouTube Metadata Scraper handle YouTube's anti-bot measures?

It treats HTTP status codes as an unreliable signal — the real block is an HTTP 200 page carrying playabilityStatus: LOGIN_REQUIRED and an empty video title, which the Actor detects directly rather than relying on 403/429/503 responses. When a page comes back blocked, the run retries on a fresh proxy session and escalates from whatever proxy mode was configured up to Apify Residential/US, which is also the schema's default.

Does YouTube Metadata Scraper extract comments and transcripts together?

Yes — every video row carries both a comments array (top-level comment text, author, like count, publish time, reply count) and a transcripts array (caption language and text or timed segments) in the same object, controlled by max_comments and include_transcripts respectively. transcripts comes back as an empty array when YouTube exposes no captions for that upload.

How many results does YouTube Metadata Scraper return per query?

Every URL in urls resolves to one row. For keywords, up to max_results_per_keyword videos are taken per phrase (default 10, maximum 500) — but YouTube's own search stops serving new results well before any high setting, at a measured ceiling of roughly 133–484 videos per keyword depending on the phrase. Comments are capped by max_comments (up to 5000) and transcript tracks are capped at 6 languages per video.

How do I use YouTube Metadata Scraper to monitor video engagement over time?

Run the same urls or keywords set on a recurring Apify Schedule, key each run's rows by id plus the run date, and alert when a tracked video's viewCount, likeCount, or commentCount moves sharply against its own previous run.

Does YouTube Metadata Scraper work with Claude, ChatGPT, and AI agent frameworks?

Yes. It's reachable through Apify's hosted MCP server at mcp.apify.com for MCP-native clients, and callable as a standard HTTP endpoint by any agent framework that can issue a request and parse JSON — which enables a grounded agent that retrieves live video, comment, and transcript data before answering a question.

How does YouTube Metadata Scraper compare to other YouTube scrapers?

As observed on the Apify Store on 2026-07-30, dz_omar/youtube-transcript-metadata-extractor extracts transcripts and metadata from direct URLs but has no keyword-search input, and its own listing documents that comment extraction is a separate paid call to a second actor in Standby Mode; apidojo/youtube-playlist-scraper accepts keywords, but only to discover playlists, and extracts neither comments nor transcripts; and beyondops/youtube-metadata-scraper-pro-v2 accepts direct video URLs only (no keyword-search field is documented) and recommends pasting a Netscape cookies.txt export to avoid anti-bot walls, with no transcript or comment extraction at all. YouTube Metadata Scraper is the only one of the three, as checked, that combines keyword search discovery, comments, and transcripts in one row, with no login cookie required.

What happens if a video is private, deleted, or blocked on every proxy exit?

It is retried and, if still unresolvable, skipped and logged with its error in the run's SUMMARY record rather than the dataset — a skipped video is never saved and never charged. If every requested video ends up in that state, the run is reported as failed rather than silently succeeding with zero rows.

Can I use YouTube Metadata Scraper without managing proxies or YouTube credentials?

You don't need a YouTube account, cookie, or credential of any kind — there is no such field in the input schema. Proxy configuration is exposed because YouTube walls most non-residential exits, so the Actor defaults proxyConfiguration to Apify Residential/US and escalates to it automatically if a lower-tier exit gets blocked mid-run; you can leave the default in place and never touch it.

💬 Your feedback

Found a bug or missing a field? Let us know through the Issues tab on this Actor's Apify Store page — it's actively monitored and helps prioritize fixes and new fields.